Multiple approaches have been proposed to improve re-ranking in open-domain QA. In [1]}, the authors use LSTMs to encode questions and answers and then perform attention- and CNN-based pooling in order to perform question-answer-matching; [2]} follows a similar idea, but produces multiple vector representations for each question and answer, which can then focus on different aspects. Other works like [3]} aim to improve the answer selection process by filtering out noisy, irrelevant paragraphs. Afterwards, the answer is selected from the remaining, relevant paragraphs. Some works have used evidence aggregation to re-rank passages based on information from multiple other passages [4]} or reinforcement learning to jointly train a ranking model to rank the passages and a reading model to extract the answer from a passage [5]}. In [6]}, the authors use weak supervision to train a BERT-based passage ranking model without any ground-truth labels.
